






"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Dict						=			[1]
						for i in range(2     ,      a__ ):
												factorials.append(factorials[-1] * i )
						assert 0 <= k < factorials[-1] * n, "k out of bounds"

						lowerCAmelCase   :Dict						=			[]
						lowerCAmelCase   :Union[str, Any]						=			list(range(a__ ) )

						# Find permutation
						while factorials:
												lowerCAmelCase   :Any						=			factorials.pop()
												lowerCAmelCase	, lowerCAmelCase   :List[str]						=			divmod(a__     ,      a__ )
												permutation.append(elements[number] )
												elements.remove(elements[number] )
						permutation.append(elements[0] )

						return permutation


if __name__ == "__main__":
		import doctest

		doctest.testmod()







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")